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sklearn/linear_model/_perceptron.py
226 строк
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Christian Lorentzen
FIX range (and default) of eta0 in SGD (#31933)
11 сен 2025, 13:59
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11 сен 2025, 13:59
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# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause from numbers import Real from sklearn.linear_model._stochastic_gradient import BaseSGDClassifier from sklearn.utils._param_validation import Interval, StrOptions class Perceptron(BaseSGDClassifier): """Linear perceptron classifier. The implementation is a wrapper around :class:`~sklearn.linear_model.SGDClassifier` by fixing the `loss` and `learning_rate` parameters as:: SGDClassifier(loss="perceptron", learning_rate="constant") Other available parameters are described below and are forwarded to :class:`~sklearn.linear_model.SGDClassifier`. Read more in the :ref:`User Guide <perceptron>`. Parameters ---------- penalty : {'l2','l1','elasticnet'}, default=None The penalty (aka regularization term) to be used. alpha : float, default=0.0001 Constant that multiplies the regularization term if regularization is used. l1_ratio : float, default=0.15 The Elastic Net mixing parameter, with `0 <= l1_ratio <= 1`. `l1_ratio=0` corresponds to L2 penalty, `l1_ratio=1` to L1. Only used if `penalty='elasticnet'`. .. versionadded:: 0.24 fit_intercept : bool, default=True Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. max_iter : int, default=1000 The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the ``fit`` method, and not the :meth:`partial_fit` method. .. versionadded:: 0.19 tol : float or None, default=1e-3 The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol). .. versionadded:: 0.19 shuffle : bool, default=True Whether or not the training data should be shuffled after each epoch. verbose : int, default=0 The verbosity level. eta0 : float, default=1 Constant by which the updates are multiplied. n_jobs : int, default=None The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. random_state : int, RandomState instance or None, default=0 Used to shuffle the training data, when ``shuffle`` is set to ``True``. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. early_stopping : bool, default=False Whether to use early stopping to terminate training when validation score is not improving. If set to True, it will automatically set aside a stratified fraction of training data as validation and terminate training when validation score is not improving by at least `tol` for `n_iter_no_change` consecutive epochs. .. versionadded:: 0.20 validation_fraction : float, default=0.1 The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True. .. versionadded:: 0.20 n_iter_no_change : int, default=5 Number of iterations with no improvement to wait before early stopping. .. versionadded:: 0.20 class_weight : dict, {class_label: weight} or "balanced", default=None Preset for the class_weight fit parameter. Weights associated with classes. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``. warm_start : bool, default=False When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See :term:`the Glossary <warm_start>`. Attributes ---------- classes_ : ndarray of shape (n_classes,) The unique classes labels. coef_ : ndarray of shape (1, n_features) if n_classes == 2 else \ (n_classes, n_features) Weights assigned to the features. intercept_ : ndarray of shape (1,) if n_classes == 2 else (n_classes,) Constants in decision function. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_iter_ : int The actual number of iterations to reach the stopping criterion. For multiclass fits, it is the maximum over every binary fit. t_ : int Number of weight updates performed during training. Same as ``(n_iter_ * n_samples + 1)``. See Also -------- sklearn.linear_model.SGDClassifier : Linear classifiers (SVM, logistic regression, etc.) with SGD training. Notes ----- ``Perceptron`` is a classification algorithm which shares the same underlying implementation with ``SGDClassifier``. In fact, ``Perceptron()`` is equivalent to `SGDClassifier(loss="perceptron", eta0=1, learning_rate="constant", penalty=None)`. References ---------- https://en.wikipedia.org/wiki/Perceptron and references therein. Examples -------- >>> from sklearn.datasets import load_digits >>> from sklearn.linear_model import Perceptron >>> X, y = load_digits(return_X_y=True) >>> clf = Perceptron(tol=1e-3, random_state=0) >>> clf.fit(X, y) Perceptron() >>> clf.score(X, y) 0.939... """ _parameter_constraints: dict = {**BaseSGDClassifier._parameter_constraints} _parameter_constraints.pop("loss") _parameter_constraints.pop("average") _parameter_constraints.update( { "penalty": [StrOptions({"l2", "l1", "elasticnet"}), None], "alpha": [Interval(Real, 0, None, closed="left")], "l1_ratio": [Interval(Real, 0, 1, closed="both")], "eta0": [Interval(Real, 0, None, closed="neither")], } ) def __init__( self, *, penalty=None, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3, shuffle=True, verbose=0, eta0=1.0, n_jobs=None, random_state=0, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, class_weight=None, warm_start=False, ): super().__init__( loss="perceptron", penalty=penalty, alpha=alpha, l1_ratio=l1_ratio, fit_intercept=fit_intercept, max_iter=max_iter, tol=tol, shuffle=shuffle, verbose=verbose, random_state=random_state, learning_rate="constant", eta0=eta0, early_stopping=early_stopping, validation_fraction=validation_fraction, n_iter_no_change=n_iter_no_change, power_t=0.5, warm_start=warm_start, class_weight=class_weight, n_jobs=n_jobs, )